Machine Learning Driven Air Flow Control For Reduced Energy Consumption of Ships
Research Project, 2021 – 2023

This project is for the first time exploring the usage of machine learning to optimize the performance of ships and vessels.
The project is aiming to address the investigation and the control of the air flow surrounding longhaul ships. The external flow has an impact on the total ship resistance and it is also responsible for natural instabilities, created for example over aft frigate deck regions, that influence safety features, such helicopter landing pads, and environmental aspects, such as avoiding smoke intake to the HVAC systems. The main tangible goals are therefore to decrease drag (resistance of motion) by 5% and develop a system which have control over the natural flow instabilities. This will be achieved with a machine learning driven design approach to drive an active flow control system

Participants

Rickard Bensow (contact)

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Sinisa Krajnovic

Chalmers, Mechanics and Maritime Sciences (M2)

Kewei Xu

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Funding

Chalmers Transport Area of Advance

Funding Chalmers participation during 2021–2023

Related Areas of Advance and Infrastructure

Sustainable development

Driving Forces

Transport

Areas of Advance

Energy

Areas of Advance

C3SE (Chalmers Centre for Computational Science and Engineering)

Infrastructure

Innovation and entrepreneurship

Driving Forces

Publications

More information

Latest update

10/2/2023